function GAD_Mid_cdf_plot_pv(Data_Summary)
%% WT saline data
G42_saline_PV = [];
for n = 1:size(Data_Summary,2)
    if strcmp(Data_Summary(n).animalName, 'G2_F6(2)_42') && strcmp(Data_Summary(n).drug,'saline')  && isempty(G42_saline_PV)
        [PV, PV_Shuffle] = get_PV_values_ready(Data_Summary(n).PV_Corr_Values, Data_Summary(n).Shuffled_PV_Corr, Data_Summary(n).PV_Corr_Coherent_Rot);
        G42_saline_PV = PV;
        G42_saline_Shuffle = PV_Shuffle;
    elseif strcmp(Data_Summary(n).animalName, 'G2_F6(2)_42') && strcmp(Data_Summary(n).drug,'saline')   && ~isempty(G42_saline_PV)
        [PV, PV_Shuffle] = get_PV_values_ready(Data_Summary(n).PV_Corr_Values, Data_Summary(n).Shuffled_PV_Corr, Data_Summary(n).PV_Corr_Coherent_Rot);
        G42_saline_PV = cat(1,G42_saline_PV,PV);
        G42_saline_Shuffle = cat(1,G42_saline_Shuffle,PV_Shuffle);
    end
end
[G42sf,G42sx,G42slow,G42shigh] = ecdf(G42_saline_PV,'Function','cdf','Alpha',0.05);
[G42ssf,G42ssx,G42sslow,G42sshigh] = ecdf(G42_saline_Shuffle,'Function','cdf','Alpha',0.05);

%% Organize data for WT saline

% get WT saline CDF mean
A = cat(1,G42sf,[]);
WT_saline_F = sort(A);

% get WT saline CDF x-steps
B = cat(1,G42sx,[]);
WT_saline_X = sort(B);

% get WT saline CDF high bound mean
C = cat(1,G42shigh,[]);
WT_saline_H = sort(C);

% get WT saline CDF low bound mean
D = cat(1,G42slow,[]);
WT_saline_L = sort(D);

%% MUT saline data
G36_saline_PV = [];
for n = 1:size(Data_Summary,2)
    if strcmp(Data_Summary(n).animalName, 'G2_F6(2)_36') && strcmp(Data_Summary(n).drug,'saline')  && isempty(G36_saline_PV)
        [PV, PV_Shuffle] = get_PV_values_ready(Data_Summary(n).PV_Corr_Values, Data_Summary(n).Shuffled_PV_Corr, Data_Summary(n).PV_Corr_Coherent_Rot);
        G36_saline_PV = PV;
        G36_saline_Shuffle = PV_Shuffle;
    elseif strcmp(Data_Summary(n).animalName, 'G2_F6(2)_36') && strcmp(Data_Summary(n).drug,'saline')   && ~isempty(G36_saline_PV)
        [PV, PV_Shuffle] = get_PV_values_ready(Data_Summary(n).PV_Corr_Values, Data_Summary(n).Shuffled_PV_Corr, Data_Summary(n).PV_Corr_Coherent_Rot);
        G36_saline_PV = cat(1,G36_saline_PV,PV);
        G36_saline_Shuffle = cat(1,G36_saline_Shuffle,PV_Shuffle);
    end
end
[G36sf,G36sx,G36slow,G36shigh] = ecdf(G36_saline_PV,'Function','cdf','Alpha',0.05);
[G36ssf,G36ssx,G36sslow,G36sshigh] = ecdf(G36_saline_Shuffle,'Function','cdf','Alpha',0.05);

%% Organize data for MUT saline

% get MUT saline CDF mean
A = cat(1,G36sf,[]);
MUT_saline_F = sort(A);

% get MUT saline CDF x-steps
B = cat(1,G36sx,[]);
MUT_saline_X = sort(B);

% get MUT saline CDF high bound mean
C = cat(1,G36shigh,[]);
MUT_saline_H = sort(C);

% get MUT saline CDF low bound mean
D = cat(1,G36slow,[]);
MUT_saline_L = sort(D);

%% WT midazolam 2.5mg/kg data
G42_mid25_PV = [];
for n = 1:size(Data_Summary,2)
    if strcmp(Data_Summary(n).animalName, 'G2_F6(2)_42') && strcmp(Data_Summary(n).drug,'Midazolam 2.5mg/kg')  && isempty(G42_mid25_PV)
        [PV, PV_Shuffle] = get_PV_values_ready(Data_Summary(n).PV_Corr_Values, Data_Summary(n).Shuffled_PV_Corr, Data_Summary(n).PV_Corr_Coherent_Rot);
        G42_mid25_PV = PV;
        G42_mid25_Shuffle = PV_Shuffle;
    elseif strcmp(Data_Summary(n).animalName, 'G2_F6(2)_42') && strcmp(Data_Summary(n).drug,'Midazolam 2.5mg/kg')   && ~isempty(G42_mid25_PV)
        [PV, PV_Shuffle] = get_PV_values_ready(Data_Summary(n).PV_Corr_Values, Data_Summary(n).Shuffled_PV_Corr, Data_Summary(n).PV_Corr_Coherent_Rot);
        G42_mid25_PV = cat(1,G42_mid25_PV,PV);
        G42_mid25_Shuffle = cat(1,G42_mid25_Shuffle,PV_Shuffle);
    end
end
[G42m25f,G42m25x,G42m25low,G42m25high] = ecdf(G42_mid25_PV,'Function','cdf','Alpha',0.05);
[G42sm25f,G42sm25x,G42sm25low,G42sm25high] = ecdf(G42_mid25_Shuffle,'Function','cdf','Alpha',0.05);

%% Organize data for WT MID 2.5mg/kg

% get WT m25 CDF mean
A = cat(1,G42m25f,[]);
WT_m25_F = sort(A);

% get WT m25 CDF x-steps
B = cat(1,G42m25x,[]);
WT_m25_X = sort(B);

% get WT m25 CDF high bound mean
C = cat(1,G42m25high,[]);
WT_m25_H = sort(C);

% get WT m25 CDF low bound mean
D = cat(1,G42m25low,[]);
WT_m25_L = sort(D);

%% MUT MID 2.5mg/kg data
G36_m25_PV = [];
for n = 1:size(Data_Summary,2)
    if strcmp(Data_Summary(n).animalName, 'G2_F6(2)_36') && strcmp(Data_Summary(n).drug,'Midazolam 2.5mg/kg')  && isempty(G36_m25_PV)
        [PV, PV_Shuffle] = get_PV_values_ready(Data_Summary(n).PV_Corr_Values, Data_Summary(n).Shuffled_PV_Corr, Data_Summary(n).PV_Corr_Coherent_Rot);
        G36_m25_PV = PV;
        G36_m25_Shuffle = PV_Shuffle;
    elseif strcmp(Data_Summary(n).animalName, 'G2_F6(2)_36') && strcmp(Data_Summary(n).drug,'Midazolam 2.5mg/kg')   && ~isempty(G36_m25_PV)
        [PV, PV_Shuffle] = get_PV_values_ready(Data_Summary(n).PV_Corr_Values, Data_Summary(n).Shuffled_PV_Corr, Data_Summary(n).PV_Corr_Coherent_Rot);
        G36_m25_PV = cat(1,G36_m25_PV,PV);
        G36_m25_Shuffle = cat(1,G36_m25_Shuffle,PV_Shuffle);
    end
end
[G36m25f,G36m25x,G36m25low,G36m25high] = ecdf(G36_m25_PV,'Function','cdf','Alpha',0.05);
[G36sm25f,G36sm25x,G36sm25low,G36sm25high] = ecdf(G36_m25_Shuffle,'Function','cdf','Alpha',0.05);

%% Organize data for MUT MID 2.5mg/kg

% get MUT m25 CDF mean
A = cat(1,G36m25f,[]);
MUT_m25_F = sort(A);

% get MUT m25 CDF x-steps
B = cat(1,G36m25x,[]);
MUT_m25_X = sort(B);

% get MUT m25 CDF high bound mean
C = cat(1,G36m25high,[]);
MUT_m25_H = sort(C);

% get MUT m25 CDF low bound mean
D = cat(1,G36m25low,[]);
MUT_m25_L = sort(D);

%% Finally, put shuffle data together

% get shuffle CDF mean
A = cat(1,G36ssf,G42ssf,G36sm25f,G42sm25f);
Shuffle_F = sort(A);

% get shuffle CDF x-steps
B = cat(1,G36ssx,G42ssx,G36sm25x,G42sm25x);
Shuffle_X = sort(B);

% % % get shuffle CDF high bound mean
% % C = cat(1,G36sshigh,G21sshigh,G45sshigh,G53sshigh,G42sshigh,G28sshigh,G31sshigh,G34sshigh,G36sm25high,G42sm25high,G21sm25high,G28sm25high,G31sm25high,G34sm25high,G45sm25high,G53sm25high);
% % Shuffle_H = sort(C);
% % 
% % % get shuffle CDF low bound mean
% % D = cat(1,G36sslow,G21sslow,G45sslow,G53sslow,G42sslow,G28sslow,G31sslow,G34sslow,G36sm25low,G42sm25low,G21sm25low,G28sm25low,G31sm25low,G34sm25low,G45sm25low,G53sm25low);
% % Shuffle_L = sort(D);

%% plot all data

figure
subplot(1,4,1)
plot(WT_saline_X,WT_saline_F,'Color','#1750AC','LineWidth',3);
hold on
% % plot(WT_saline_X,WT_saline_L,'--k','LineWidth',1);
% % hold on
% % plot(WT_saline_X,WT_saline_H,'--k','LineWidth',1);
% % hold on
plot(MUT_saline_X,MUT_saline_F,'Color','#F53BD6','LineWidth',3);
hold on
% % plot(MUT_saline_X,MUT_saline_L,'--k','LineWidth',1);
% % hold on
% % plot(MUT_saline_X,MUT_saline_H,'--k','LineWidth',1);
% % hold on
plot(Shuffle_X,Shuffle_F,'-k','LineWidth',3);
hold on
% % plot(Shuffle_X,Shuffle_L,'--k','LineWidth',1);
% % hold on
% % plot(Shuffle_X,Shuffle_H,'--k','LineWidth',1);
% % hold on
% % legend('p-WT Saline','','','\alpha5-i-KO Saline','','','Shuffle','95% Confidence Interval','','Location','southeast');
xlabel('PV Correlation Value')
ylabel('Cumulative Fraction of Locations')
xlim([-0.6,1])
set(gca,'FontSize',10)
set(gca, 'visible', 'off')
hold off

subplot(1,4,2)
plot(WT_saline_X,WT_saline_F,'Color','#1750AC','LineWidth',3);
hold on
% % plot(WT_saline_X,WT_saline_L,'--k','LineWidth',1);
% % hold on
% % plot(WT_saline_X,WT_saline_H,'--k','LineWidth',1);
% % hold on
plot(WT_m25_X,WT_m25_F,'Color','#73B9EE' ,'LineWidth',3);
hold on
% % plot(WT_m25_X,WT_m25_L,'--k','LineWidth',1);
% % hold on
% % plot(WT_m25_X,WT_m25_H,'--k','LineWidth',1);
% % hold on
plot(Shuffle_X,Shuffle_F,'-k','LineWidth',3);
hold on
% % plot(Shuffle_X,Shuffle_L,'--k','LineWidth',1);
% % hold on
% % plot(Shuffle_X,Shuffle_H,'--k','LineWidth',1);
% % hold on
% % legend('p-WT Saline','','','p-WT Etom 7mg/kg','','','Location','southeast');
xlabel('PV Correlation Value')
ylabel('Cumulative Fraction of Locations')
xlim([-0.6,1])
set(gca,'FontSize',10)
set(gca, 'visible', 'off')
hold off

subplot(1,4,3)
plot(MUT_saline_X,MUT_saline_F,'Color','#F53BD6','LineWidth',3);
hold on
% % plot(MUT_saline_X,MUT_saline_L,'--k','LineWidth',1);
% % hold on
% % plot(MUT_saline_X,MUT_saline_H,'--k','LineWidth',1);
% % hold on
plot(MUT_m25_X,MUT_m25_F,'Color','#FA9CEA' ,'LineWidth',3);
hold on
% % plot(MUT_m25_X,MUT_m25_L,'--k','LineWidth',1);
% % hold on
% % plot(MUT_m25_X,MUT_m25_H,'--k','LineWidth',1);
% % hold on
plot(Shuffle_X,Shuffle_F,'-k','LineWidth',3);
hold on
% % plot(Shuffle_X,Shuffle_L,'--k','LineWidth',1);
% % hold on
% % plot(Shuffle_X,Shuffle_H,'--k','LineWidth',1);
% % hold on
% % legend('\alpha5-i-KO Saline','','','\alpha5-i-KO Etom 7mg/kg','','','Location','southeast');
xlabel('PV Correlation Value')
ylabel('Cumulative Fraction of Locations')
xlim([-0.6,1])
set(gca,'FontSize',10)
set(gca, 'visible', 'off')
hold off

subplot(1,4,4)
plot(WT_m25_X,WT_m25_F,'Color','#73B9EE' ,'LineWidth',3);
hold on
% % plot(WT_m25_X,WT_m25_L,'--k','LineWidth',1);
% % hold on
% % plot(WT_m25_X,WT_m25_H,'--k','LineWidth',1);
% % hold on
plot(MUT_m25_X,MUT_m25_F,'Color','#FA9CEA' ,'LineWidth',3);
hold on
% % plot(MUT_m25_X,MUT_m25_L,'--k','LineWidth',1);
% % hold on
% % plot(MUT_m25_X,MUT_m25_H,'--k','LineWidth',1);
% % hold on
plot(Shuffle_X,Shuffle_F,'-k','LineWidth',3);
hold on
% % plot(Shuffle_X,Shuffle_L,'--k','LineWidth',1);
% % hold on
% % plot(Shuffle_X,Shuffle_H,'--k','LineWidth',1);
% % hold on
% % legend('p-WT Etom 7mg/kg','','','\alpha5-i-KO Etom 7mg/kg','','','Location','southeast');
xlabel('PV Correlation Value')
ylabel('Cumulative Fraction of Locations')
xlim([-0.6,1])
set(gca,'FontSize',10)
set(gca, 'visible', 'off')
hold off


% % %% Clean Up
% % cutoff = -0.1;
% % index = nan(size(WT_saline_X,1),1);
% % for n = 1:size(WT_saline_X,1)
% %     if WT_saline_X(n,1) >= cutoff
% %         index(n,1) = n;
% %     end
% % end
% % index = min(index);
% % WT_saline_F = WT_saline_F(index:end,1);
% % 
% % index = nan(size(WT_m25_X,1),1);
% % for n = 1:size(WT_m25_X,1)
% %     if WT_m25_X(n,1) >= cutoff
% %         index(n,1) = n;
% %     end
% % end
% % index = min(index);
% % WT_m25_F = WT_m25_F(index:end,1);
% % 
% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% % 
% % index = nan(size(MUT_saline_X,1),1);
% % for n = 1:size(MUT_saline_X,1)
% %     if MUT_saline_X(n,1) >= cutoff
% %         index(n,1) = n;
% %     end
% % end
% % index = min(index);
% % MUT_saline_F = MUT_saline_F(index:end,1);
% % 
% % index = nan(size(MUT_m25_X,1),1);
% % for n = 1:size(MUT_m25_X,1)
% %     if MUT_m25_X(n,1) >= cutoff
% %         index(n,1) = n;
% %     end
% % end
% % index = min(index);
% % MUT_m25_F = MUT_m25_F(index:end,1);
% % 
% % %% Further clean up
% % 
% % index = nan(size(G42sx,1),1);
% % for n = 1:size(G42sx,1)
% %     if G42sx(n,1) >= cutoff
% %         index(n,1) = n;
% %     end
% % end
% % index = min(index);
% % G42sf = G42sf(index:end,1);
% % 
% % index = nan(size(G28sx,1),1);
% % for n = 1:size(G28sx,1)
% %     if G28sx(n,1) >= cutoff
% %         index(n,1) = n;
% %     end
% % end
% % index = min(index);
% % G28sf = G28sf(index:end,1);
% % 
% % index = nan(size(G31sx,1),1);
% % for n = 1:size(G31sx,1)
% %     if G31sx(n,1) >= cutoff
% %         index(n,1) = n;
% %     end
% % end
% % index = min(index);
% % G31sf = G31sf(index:end,1);
% % 
% % index = nan(size(G34sx,1),1);
% % for n = 1:size(G34sx,1)
% %     if G34sx(n,1) >= cutoff
% %         index(n,1) = n;
% %     end
% % end
% % index = min(index);
% % G34sf = G34sf(index:end,1);
% % 
% % 
% % %%%%%%%%%%%%%%%%%%%
% % %%%%%%%%%%%%%%%%%%%
% % 
% % index = nan(size(G36sx,1),1);
% % for n = 1:size(G36sx,1)
% %     if G36sx(n,1) >= cutoff
% %         index(n,1) = n;
% %     end
% % end
% % index = min(index);
% % G36sf = G36sf(index:end,1);
% % 
% % index = nan(size(G21sx,1),1);
% % for n = 1:size(G21sx,1)
% %     if G21sx(n,1) >= cutoff
% %         index(n,1) = n;
% %     end
% % end
% % index = min(index);
% % G21sf = G21sf(index:end,1);
% % 
% % index = nan(size(G45sx,1),1);
% % for n = 1:size(G45sx,1)
% %     if G45sx(n,1) >= cutoff
% %         index(n,1) = n;
% %     end
% % end
% % index = min(index);
% % G45sf = G45sf(index:end,1);
% % 
% % index = nan(size(G53sx,1),1);
% % for n = 1:size(G53sx,1)
% %     if G53sx(n,1) >= cutoff
% %         index(n,1) = n;
% %     end
% % end
% % index = min(index);
% % G53sf = G53sf(index:end,1);
% % 
% % 
% % 
% % %% Perform KS statistics tests
% % 
% % % test difference between wt saline & mut saline
% % [test_result1, p_value1] = kstest2(WT_saline_F,MUT_saline_F);
% % if test_result1 == 0
% %     disp('WT vs MUT saline are not different')
% % elseif test_result1 == 1
% %     disp('WT vs MUT saline are different')
% % end
% % p1 = string(p_value1);
% % disp(strcat('WT vs MUT saline KS test p-value =',{' '}, p1));
% % 
% % % test between wt saline & wt etom 7mg/kg
% % [test_result2, p_value2] = kstest2(WT_saline_F,WT_m25_F);
% % if test_result2 == 0
% %     disp('WT saline vs WT ETOM 7mg/kg are not different')
% % elseif test_result2 == 1
% %     disp('WT saline vs WT ETOM 7mg/kg are different')
% % end
% % p2 = string(p_value2);
% % disp(strcat('WT saline vs WT ETOM 7mg/kg KS test p-value =',{' '}, p2));
% % 
% % % test between mut saline & mut etom 7mg/kg
% % [test_result3, p_value3] = kstest2(MUT_saline_F,MUT_m25_F);
% % if test_result3 == 0
% %     disp('MUT saline vs MUT ETOM 7mg/kg are not different')
% % elseif test_result3 == 1
% %     disp('MUT saline vs MUT ETOM 7mg/kg are different')
% % end
% % p3 = string(p_value3);
% % disp(strcat('MUT saline vs MUT ETOM 7mg/kg KS test p-value =',{' '}, p3));
% % 
% % % test difference between wt saline & mut etom 7mg/kg
% % [test_result4, p_value4] = kstest2(WT_m25_F,MUT_m25_F);
% % if test_result4 == 0
% %     disp('WT vs MUT ETOM 7mg/kg are not different')
% % elseif test_result4 == 1
% %     disp('WT vs MUT ETOM 7mg/kg are different')
% % end
% % p4 = string(p_value4);
% % disp(strcat('WT vs MUT ETOM 7mg/kg KS test p-value =',{' '}, p4));




% % %% Get some KS-statistics (everything in reference to WT Saline Mean) and perform 2-way ANOVA 
% % 
% % % get WT saline KS-statistics
% % [~,~,G42sKS] = kstest2(WT_saline_F, G42sf);
% % [~,~,G28sKS] = kstest2(WT_saline_F, G28sf);
% % [~,~,G31sKS] = kstest2(WT_saline_F, G31sf);
% % [~,~,G34sKS] = kstest2(WT_saline_F, G34sf);
% % 
% % % get MUT saline KS-statistics
% % [~,~,G36sKS] = kstest2(WT_saline_F, G36sf);
% % [~,~,G21sKS] = kstest2(WT_saline_F, G21sf);
% % [~,~,G45sKS] = kstest2(WT_saline_F, G45sf);
% % [~,~,G53sKS] = kstest2(WT_saline_F, G53sf);
% % 
% % % get WT ETOM 7mg/kg KS-statistics
% % [~,~,G42m25KS] = kstest2(WT_saline_F, G42m25f);
% % [~,~,G28m25KS] = kstest2(WT_saline_F, G28m25f);
% % [~,~,G31m25KS] = kstest2(WT_saline_F, G31m25f);
% % [~,~,G34m25KS] = kstest2(WT_saline_F, G34m25f);
% % 
% % % get MUT ETOM 7mg/kg KS-statistics
% % [~,~,G36m25KS] = kstest2(WT_saline_F, G36m25f);
% % [~,~,G21m25KS] = kstest2(WT_saline_F, G21m25f);
% % [~,~,G45m25KS] = kstest2(WT_saline_F, G45m25f);
% % [~,~,G53m25KS] = kstest2(WT_saline_F, G53m25f);
% % 
% % % Plot the KS statistics
% % figure
% % A = categorical({'p-WT Saline'});
% % B = categorical({'\alpha5-i-KO Saline'});
% % C = categorical({'p-WT ETOM 7mg/kg'});
% % D = categorical({'\alpha5-i-KO ETOM 7mg/kg'});
% % X = cat(2,A,B,C,D);
% % X = reordercats(X, {'p-WT Saline','\alpha5-i-KO Saline','p-WT ETOM 7mg/kg','\alpha5-i-KO ETOM 7mg/kg'});
% % Y(1,1) = mean([G42sKS,G28sKS,G31sKS,G34sKS]);
% % Y(1,2) = mean([G36sKS,G21sKS,G45sKS,G53sKS]);
% % Y(1,3) = mean([G42m25KS,G28m25KS,G31m25KS,G34m25KS]);
% % Y(1,4) = mean([G36m25KS,G21m25KS,G45m25KS,G53m25KS]);
% % h=bar(X(1,1),Y(1,1),0.2,'LineWidth',1.2,'FaceColor','flat','FaceAlpha',0.5);
% % h.CData(1,:) = [0 0 1]; % color up each individual bar
% % ylabel('KS-statistics (vs. p-WT Saline Mean)');
% % hold on
% % h=bar(X(1,2),Y(1,2),0.2,'LineWidth',1.2,'FaceColor','flat','FaceAlpha',0.5);
% % h.CData(1,:) = [0 1 0]; % color up each individual bar
% % hold on
% % h=bar(X(1,3),Y(1,3),0.2,'LineWidth',1.2,'FaceColor','flat','FaceAlpha',0.5);
% % h.CData(1,:) = [1 0 0]; % color up each individual bar
% % hold on
% % h=bar(X(1,4),Y(1,4),0.2,'LineWidth',1.2,'FaceColor','flat','FaceAlpha',0.5);
% % h.CData(1,:) = [1 0 1]; % color up each individual bar
% % hold on
% % err(1,1)=std([G42sKS,G28sKS,G31sKS,G34sKS]);
% % err(1,2)=std([G36sKS,G21sKS,G45sKS,G53sKS]);
% % err(1,3)=std([G42m25KS,G28m25KS,G31m25KS,G34m25KS]);
% % err(1,4)=std([G36m25KS,G21m25KS,G45m25KS,G53m25KS]);
% % er = errorbar(X,Y,[],err,'LineWidth',3);    
% % er.Color = [0 0 0];                            
% % er.LineStyle = 'None'; 
% % set(gca,'FontSize',10);
% % hold on
% % scatter(A,G42sKS,70,'o','k','LineWidth',3);
% % hold on
% % scatter(A,G28sKS,70,'^','k','LineWidth',3);
% % hold on
% % scatter(A,G31sKS,70,'s','k','LineWidth',3);
% % hold on
% % scatter(A,G34sKS,70,'d','k','LineWidth',3);
% % hold on
% % scatter(B,G36sKS,70,'>','k','LineWidth',3);
% % hold on
% % scatter(B,G21sKS,70,'<','k','LineWidth',3);
% % hold on
% % scatter(B,G45sKS,70,'*','k','LineWidth',3);
% % hold on
% % scatter(B,G53sKS,70,'h','k','LineWidth',3);
% % hold on
% % scatter(C,G42m25KS,70,'o','k','LineWidth',3);
% % hold on
% % scatter(C,G28m25KS,70,'^','k','LineWidth',3);
% % hold on
% % scatter(C,G31m25KS,70,'s','k','LineWidth',3);
% % hold on
% % scatter(C,G34m25KS,70,'d','k','LineWidth',3);
% % hold on
% % scatter(D,G36m25KS,70,'>','k','LineWidth',3);
% % hold on
% % scatter(D,G21m25KS,70,'<','k','LineWidth',3);
% % hold on
% % scatter(D,G45m25KS,70,'*','k','LineWidth',3);
% % hold on
% % scatter(D,G53m25KS,70,'h','k','LineWidth',3);
% % ylim([0,0.008])
% % hold off
% % 
% % % perform 2-way ANOVA
% % % Data table organized as: 
% % % column = drug conditions (saline & ETOM 7mg/kg)
% % % row = genotypes (p-WT & MUT), four animals of each genotype
% % % Therefore the data table is 8-by-2
% % 
% % WT_saline_KS = ([G42sKS,G28sKS,G31sKS,G34sKS]);
% % MUT_saline_KS = ([G36sKS,G21sKS,G45sKS,G53sKS]);
% % WT_m25_KS = ([G42m25KS,G28m25KS,G31m25KS,G34m25KS]);
% % MUT_m25_KS = ([G36m25KS,G21m25KS,G45m25KS,G53m25KS]);
% % 
% % KS_Summary(:,1) = cat(1,WT_saline_KS(:),MUT_saline_KS(:));
% % KS_Summary(:,2) = cat(1,WT_m25_KS(:),MUT_m25_KS(:));
% % 
% % [KS_p_value,KS_anova_table] = anova2(KS_Summary,4);


end